Community Clustering for Distributed Publish/Subscribe Systems

Provided by: University of Toledo
Topic: Big Data
Format: PDF
Optimized placement of clients in a distributed publish/subscribe system is an important technique to improve overall system efficiency. Current methods, like interest clustering or publisher placement, treat a client as, either a pure publisher, or subscriber, but not as both. Also, the cost of client movement is usually ignored. However, many applications based on publish/subscribe systems model clients as publisher and subscriber at the same time, which breaks the assumptions made by current approaches. Considering the complex dependency among clients, the authors propose a new community-oriented clustering approach, based on the forming of client clusters that exhibit intense communication relationships, while keeping client movement cost low.

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